Person Re-identification with Patch-Based Local Sparse Matching and Metric Learning

摘要

Recently, patch based matching has been demonstrated effectively to address the spatial misalignment issue caused by camera-view changes or human pose variations in person re-identification (Re-ID) problem. In this paper, we propose a novel local sparse matching model to obtain a reliable patch-wise matching for Re-ID problem. In particular, in the training phase, we develop a robust Local Sparse Matching model to learn more precise corresponding relationship between patches of positive sample image pairs. In the testing phase, we adopt a local-global distance metric learning for Re-ID task by considering global and local information simultaneously. Extensive experiments on four benchmarks demonstrate the effectiveness of our approach.